FFENet: Learning Frequency Features for Low-Light Enhancement

Zhenbing Liu, Junming Gong, Haoxiang Lu, Xipeng Pan, Rushi Lan

Abstract: The visualization and analysis of images captured under low-light conditions have long been a significant challenge in various fields, including surveillance, astronomy, medical imaging, and remote sensing. This work presents a new low-light image enhancement (LLIE) method called frequency features enhancement network (FFENet). We can observe that the image contains different levels of information at high and low frequencies, so we can achieve more accurate enhancement by processing the high and low frequencies separately according to their different information characteristics. Specifically, we first obtain the high- and low-frequency information of the image through the frequency decomposition (FD) block. Then, we use a contrast stretch subnetwork (CSNet) to realize the enhancement of the content of low frequency and apply a high-frequency enhancement subnetwork (HENet) to complete the detail extraction and noise suppression of high frequency. Finally, in the fusion stage, the network obtains images with normal illumination. Extensive experimental results suggest that our FFENet superiors several state-of-the-art approaches benefiting from its stronger multiscale feature expression and extraction ability. In addition, experiments also indicate that object recognition and other high-level computer applications on low-light images can be promoted owing to our designed FFENet method.